US endorses self-driving cars, with a catch: Feds want to control tech approval, not states

Lexus SUV Google prototype autonomous vehicle in Mountain View, 2015.  REUTERS

Federal auto safety regulators today said that self-driving cars “will save time, money and lives,” but also sent a clear signal that they want the power to inspect and approve technology before it hits the highways, rather than each U.S. state setting its own safety standards.

U.S. Transportation Secretary Anthony Foxx said on a press call today that a new federal premarket approval system "would require a lot more upfront discussion, dialogue and staffing on our part."

The government's statement today is big news for Uber, Google, Apple, and other Silicon Valley firms pouring millions of R&D dollars into figuring out how to swap human drivers for smart machines, or at least allow us to share control in “semiautonomous” setups.

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Jigsaw: "wildly ambitious" Google spin-out aimed at tackling "surveillance, extremist indoctrination, censorship"

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Technologists have a dismal pattern: when it comes to engineering challenges ("build a global-scale comms platform") they rub their hands together with excitement; when it comes to the social challenges implied by the engineering ones ("do something about trolls") they throw their hands up and declare the problem to be too hard to solve. Read the rest

Rules for trusting "black boxes" in algorithmic control systems

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Tim O'Reilly writes about the reality that more and more of our lives -- including whether you end up seeing this very sentence! -- is in the hands of "black boxes": algorithmic decision-makers whose inner workings are a secret from the people they affect. Read the rest

Machine learning system can descramble pixelated/blurred redactions 83% of the time

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A joint UT Austin/Cornell team has taught a machine learning system based on the free/open Torch library to correctly guess the content of pixellated or blurred redactions with high accuracy: for masked faces that humans correctly guess 0.19% of the time, the system can make a correct guess 83% of the time, when given five tries. Read the rest

We need a new science of measurement to figure out when AI is working and why it fails

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Writing on Medium, AI researcher Kate Crawford (previously) and Simply Secure (previously) co-founder Meredith Whittaker make the case for a new scholarly discipline that "measures and assesses the social and economic effects of current AI systems." Read the rest

Deep dreaming at the grocery store

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Take one trip to the grocers, add a couple passes through Google's psychedelic Deep Dreaming AI-based apophenia algorithm, and voila! You've materialized the extraordinary latent in the mundane. (via Beyond the Beyond) Read the rest

Sampling bias: how a machine-learning beauty contest awarded nearly all prizes to whites

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If you've read Cathy O'Neil's Weapons of Math Destruction (you should, right NOW), then you know that machine learning can be a way to apply a deadly, nearly irrefutable veneer of objectivity to our worst, most biased practices. Read the rest

This Podcast Was Written by an AI

Today we do something weird, in honor of the end of the second season of Flash Forward! Instead of coming up with a future and then finding experts to talk about it, I asked an AI to write a future for us. And the AI apparently wants us to talk about space travel, witches, and the occult.

Flash Forward: RSS | iTunes | Twitter | Facebook | Web | Patreon | Reddit

In this episode we feed all the past Flash Forward episodes to a neural network, and ask it to write a script for us. And that script is full of space travel, Mars conspiracy theories, future witches, and a whole lot of theories about cutting someone’s hands off.

▹▹ Full show notes

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Machine-learning model fed web content makes racist and sexist associations

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Human biases exposed by Implicit Association Tests can be replicated in machine learning using GloVe word embedding, according to a new study where GloVe was trained on "a corpus of text from the Web." Read the rest

First music video created in Prisma app

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You've probably seen a few profile pics processed with the Prisma app. Now you can watch Drive like Maria's new music video, the first created entirely with the app. Read the rest

Forget Skynet: AI is already making things terrible for people who aren't rich white dudes

Kate Crawford (previously) takes to the New York Times's editorial page to ask why rich white guys act like the big risk of machine-learning systems is that they'll evolve into Skynet-like apex-predators that subjugate the human race, when there are already rampant problems with machine learning: algorithmic racist sentencing, algorithmic, racist and sexist discrimination, algorithmic harassment, algorithmic hiring bias, algorithmic terrorist watchlisting, algorithmic racist policing, and a host of other algorithmic cruelties and nonsense, each one imbued with unassailable objectivity thanks to its mathematical underpinnings. Read the rest

How facial recognition works (and how to hack your own in Python)

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You could not ask for a clearer, easier-to-read, more informative guide to facial recognition and machine learning thank Adam Geitgey's article, which is the latest in a series of equally clear explainers on machine learning, aimed at non-technical people -- and if you are a programmer, he's got links to Python sample source and projects you can use to develop your own versions. Read the rest

White House contends with AI's big social challenges, July 7/NYC

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Meredith from Simply Secure writes, "Artificial Intelligence is already with us, and the White House and New York University’s Information Law Institute are hosting a major public symposium to face what the social and economic impacts might be. AI Now, happening July 7th in New York City, will address the real world impacts of AI systems in the next next 5-10 years." Read the rest

Google is restructuring to put machine learning at the core of all it does

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Steven Levy is in characteristic excellent form in a long piece on Medium about the internal vogue for machine learning at Google; drawing on the contacts he made with In the Plex, his must-read 2012 biography of the company, Levy paints a picture of a company that's being utterly remade around newly ascendant machine learning techniques. Read the rest

Cataloging the problems facing AI researchers is a cross between a parenting manual and a management book

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Concrete Problems in AI Safety, an excellent, eminently readable paper from a group of Google AI researchers and some colleagues, sets out five hard problems facing the field: robots might damage their environments to attain their goals; robots might figure out how to cheat to attain their goals; supervising robots all the time is inefficient; robots that are allowed to try novel strategies might cause disasters; and robots that are good at one task might inappropriately try to apply that expertise to another unrelated task. Read the rest

Emojibot uses deep learning to synthesize expressive new nonverbal communications

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Dango is a personal assistant that feeds its users' messages into a deep-learning neural net to discover new expressive possibilities for emojis, GIFs and stickers, and then suggests never-seen combinations of graphic elements to your text messages that add striking nuances to them. Read the rest

Award-winning short sf film written by an AI is pretty good

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Director Oscar Sharp and AI researcher Ross Goodwin trained a machine-learning system with a huge pile of classic science fiction screenplays and turned it loose to write a short film. What emerged was an enigmatic 9-minute movie called Sunspring, which has just won Sci-Fi London's 48-hour challenge. Read the rest

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